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Gleif

by io.github.pipeworx-io
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Gleif Lei Screen

gleif_lei_screen
Read-onlyIdempotent

Bulk screen of LEI registrations in a jurisdiction across all ~3.4M LEIs in the GLEIF golden copy. Filter by registration status (LAPSED, RETIRED, ANNULLED, ISSUED ...), entity status, category, most recent legal-entity event (MERGERS_AND_ACQUISITIONS, ABSORPTION, DISSOLUTION, LIQUIDATION, CHANGE_LEGAL_NAME ...), a name fragment, and a lookback window in days. Answers "which LEIs in Germany lapsed in the last 30 days", "Delaware entities retired this quarter", "UK entities with a merger event in the last 90 days", "new LEIs issued in Luxembourg this week". The window applies to status_date — the date the current status took effect: LAPSED = the renewal date that was missed, RETIRED/ANNULLED = entity expiration date (else last update), ISSUED = initial registration — or to the event date when event_type is given. Returns total (exact up to 100,000) plus a page of rows, newest first, with data_as_of. Sole-proprietor names are withheld (personal data); call get_lei for one record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows per page, 1-500 (default 100).
offsetNoRows to skip for paging (default 0).
categoryNoEntity category.
event_typeNoThe MOST RECENT legal-entity event recorded on the LEI (earlier events are not matched). When given, since_days applies to the event date instead of status_date. A merger shows up as RETIRED plus MERGERS_AND_ACQUISITIONS or ABSORPTION, and successor_lei names the survivor.
since_daysNoOnly entities whose status_date (or event date, with event_type) falls within the last N days, 1-3650.
jurisdictionYesLegal jurisdiction: ISO 3166-1 alpha-2 country ("DE", "GB", "US") or a subdivision ("US-DE", "CA-ON"). A country code includes its subdivisions. Common country names are converted.
entity_statusNoLegal entity status.
name_containsNoCase-insensitive fragment of the legal or other name, at least 3 characters, e.g. "bank".
registration_statusNoLEI registration status. LAPSED = renewal missed (the company may still exist); RETIRED = the entity ceased, including by merger (pair with event_type to tell which).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description goes well beyond them: it discloses the 100,000 total cap, that rows are returned newest first with data_as_of, and that sole-proprietor names are withheld as personal data. It also clarifies the status_date semantics per registration status. This is rich behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the operation and scope, then filters, then return behavior. Dense but every sentence carries information; the example questions are slightly redundant given the enumerated filters, which keeps it from a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description explains the return shape (total plus a page of rows, newest first, with data_as_of) and the pagination parameters are documented in the schema. Nothing an agent needs to invoke this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds cross-parameter meaning not spelled out in the schema: that since_days applies to the event date when event_type is given, and how LAPSED/RETIRED differ in what date the window hits. That interaction is genuinely useful beyond the schema's per-field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource+scope: 'Bulk screen of LEI registrations in a jurisdiction across all ~3.4M LEIs in the GLEIF golden copy.' It distinguishes itself from the single-record sibling by ending with 'call get_lei for one record.' An agent can tell it apart from search_lei or get_lei without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides concrete example questions ('which LEIs in Germany lapsed in the last 30 days') that make the intended use obvious, and explicitly routes single-record needs to get_lei. It does not, however, contrast itself with search_lei, which a sibling of similar scope, so the exclusion set is incomplete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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